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Found 8 papers, 2 papers with code
Multiscale Hodge Scattering Networks for Data Analysis
Our construction is based on multiscale basis dictionaries on simplicial complexes, i. e., the $\kappa$-GHWT and $\kappa$-HGLET, which we recently developed for simplices of dimension $\kappa \in \mathbb{N}$ in a given simplicial complex by generalizing the node-based Generalized Haar-Walsh Transform (GHWT) and Hierarchical Graph Laplacian Eigen Transform (HGLET).
The Scattering Transform Network with Generalized Morse Wavelets and Its Application to Music Genre Classification
We propose to use the Generalized Morse Wavelets (GMWs) instead of commonly-used Morlet (or Gabor) wavelets in the Scattering Transform Network (STN), which we call the GMW-STN, for signal classification problems.
Monogenic Wavelet Scattering Network for Texture Image Classification
The scattering transform network (STN), which has a similar structure as that of a popular convolutional neural network except its use of predefined convolution filters and a small number of layers, can generates a robust representation of an input signal relative to small deformations.
eGHWT: The Extended Generalized Haar-Walsh Transform
This article describes the details of the eGHWT best-basis algorithm and demonstrates its superiority using several examples including genuine graph signals as well as conventional digital images viewed as graph signals.
Natural Graph Wavelet Packet Dictionaries
We introduce a set of novel multiscale basis transforms for signals on graphs that utilize their "dual" domains by incorporating the "natural" distances between graph Laplacian eigenvectors, rather than simply using the eigenvalue ordering.
The Use of Mutual Coherence to Prove $\ell^1/\ell^0$-Equivalence in Classification Problems
We consider the decomposition of a signal over an overcomplete set of vectors.
General Classification
Sparse Representation-based Classification
Underwater object classification using scattering transform of sonar signals
In this paper, we apply the scattering transform (ST), a nonlinear map based off of a convolutional neural network (CNN), to classification of underwater objects using sonar signals.
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis
The dictionary in SRC is replaced by a local dictionary that adapts to the test sample and includes training samples and their corresponding tangent basis vectors.
